Scholarly Works, Population Health Sciences
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Research articles, presentations, and other scholarship
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Browsing Scholarly Works, Population Health Sciences by Content Type "Book chapter"
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- Coupled Human-Natural Modeling for Hydroelectric Development: Understanding the Health Impacts of America’s Renewable Energy ImportsCalder, Ryan S. D. (Duke University, 2019)Hydropower accounts for 71% of renewable electrical generation worldwide, and installed capacity may more than double by 2050. Major hydroelectric projects involve construction of reservoirs to buffer the periodicity of river discharge, meaning hydropower typically does not suffer from supply intermittency of other renewables such as wind and solar. Meanwhile, average greenhouse gas emissions are likely substantially lower than fossil fuel alternatives per unit energy produced. Domestic hydropower production in the United States is unlikely to increase substantially in the foreseeable future, but imports from Canada play an increasingly important role in achieving renewable energy targets in northern U.S. markets....
- Muskrat Falls: Methylmercury, food security, and Canadian hydroelectric developmentCalder, Ryan S. D.; Schartup, Amina T.; Bell, Trevor; Sunderland, Elsie M. (Memorial University Press, 2021-12-15)
- Studies in Big Data Series: Internet of Things and Big Data Technologies for Next Generation HealthcareAbbas, Kaja M.; Manogaran, Gunasekaran; Thota, Chandu; Lopez, Daphne; Vijayakumar, V.; Sundarsekar, Revathi (2017)The health care systems are rapidly adopting large amounts of data, driven by record keeping, compliance and regulatory requirements, and patient care. The advances in healthcare system will rapidly enlarge the size of the health records that are accessible electronically. Concurrently, fast progress has been made in clinical analytics. For example, new techniques for analyzing large size of data and gleaning new business insights from that analysis is part of what is known as big data. Big data also hold the promise of supporting a wide range of medical and healthcare functions, including among others disease surveillance, clinical decision support and population health management. Hence, effective big data based knowledge management system is needed for monitoring of patients and identify the clinical decisions to the doctor. The chapter proposes a big data based knowledge management system to develop the clinical decisions. The proposed knowledge system is developed based on variety of databases such as Electronic Health Record (EHR), Medical Imaging Data, Unstructured Clinical Notes and Genetic Data. The proposed methodology asynchronously communicates with different data sources and produces many alternative decisions to the doctor.